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Reinforcement Learning for Chemical Ordering in Alloy Nanoparticles.

Jonas Elsborg^1,2, Emma Lei Hovmand¹, Arghya Bhowmik^1,2

¹Department of Energy Conversion and Storage, Technical University of Denmark, Kongens Lyngby 2800, Denmark.

ACS Materials Au

|July 11, 2026

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This study introduces reinforcement learning (RL) to find optimal element arrangements in bimetallic alloy nanoparticles. The developed RL agent successfully identifies ground-state structures, demonstrating a transferable optimization strategy.

Area of Science:

Materials Science
Computational Chemistry
Nanotechnology

Background:

Determining optimal element ordering in bimetallic alloy nanoparticles (NPs) is crucial for their properties.
Combinatorial complexity makes traditional search methods inefficient for large nanoparticle systems.

Purpose of the Study:

To frame the search for optimal element ordering in bimetallic alloy NPs as a reinforcement learning (RL) problem.
To develop and train an RL agent capable of performing global optimization of NP structures.

Main Methods:

Utilized a reinforcement learning (RL) approach with a geometric graph representation for NPs.
Trained an RL agent using composition-conserving atomic swap actions on icosahedral nanoparticle structures.
Employed randomized Ag_XAu_309‑X compositions and orderings for agent training.

Keywords:

alloy nanoparticle chemical ordering global structure optimization graph neural network reinforcement learning

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Main Results:

The trained RL agent successfully discovered known ground-state structures for bimetallic NPs.
Optimization demonstrated robustness across varied initial NP orderings.
The RL policy showed effective extrapolation to NPs of unobserved sizes.
Limited efficacy was observed when dealing with multiple alloying elements.

Conclusions:

Reinforcement learning, combined with pretrained equivariant graph encodings, can effectively navigate complex ordering spaces in nanoparticles.
This approach offers a transferable optimization strategy with potential for generalization across compositions.
The RL method can significantly reduce the cost associated with repeated individual searches for optimal NP structures.